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Summary
Claude Opus 4.1 from Anthropic scored 62.8 on LiveCodeBench, placing it top 10 (rank 8 of 43) on this benchmark. This places the model in the competitive band for LiveCodeBench. Above the cost-effective threshold for most production workloads. Pair with a step-level evaluation harness for agent use cases.
Model details
- Provider: Anthropic
- Model key:
anthropic/claude-opus-4.1 - Context length: 200,000 tokens
- License: Proprietary
- Open weights: no
Benchmark methodology
Benchmark goal: Evaluate the LLM's ability to understand and generate correct code for programming problems, focusing on logic, syntax, and problem-solving skills.
Scoring metrics:
- Pass Rate: Percentage of problems for which the LLM generates a correct program that passes all test cases.
- Execution Time: Time taken by the LLM to generate the code, reflecting efficiency.
- Code Readability: Qualitative assessment of the generated code based on clarity, structure, and use of meaningful variable names.
Analysis
Key takeaways:
- Claude Opus demonstrates a capacity for code completion, but performance is bottlenecked by problem-solving ability.
- The generated code is often syntactically correct, but may have implementation flaws, or is only partially correct.
- Further improvements are needed in understanding complex mathematical insights.
Failure modes observed
Common failure modes:
- Incorrect logic in implementation, leading to failing test cases.
- Inability to handle edge cases, particularly division by zero or incorrect array access.
- Errors in grouping/categorizing similar elements.
Example: In the Slavic birthday present problem, the model correctly identifies the need to add 1 to the smallest digit but fails to generalize the approach to all possible cases for optimality. Additionally, in the Black and White Cells Problem, the model failed to create a covering with k sized covering cells.
Secondary metrics
- Readability score: 0.0
- Toxicity score: 0.000
- Ethics score: 0.000
Run this evaluation yourself
LayerLens evaluates Claude Opus 4.1 continuously across 11+ benchmarks. To replicate this LiveCodeBench evaluation on your own model, traces, or a different benchmark configuration, open the model in $LayerLens/$.
Source: LayerLens evaluation 690946bd228531518d5fcaf6. Updated 2025-11-04.